Computational Methods for Resting-State EEG of Patients With Disorders of Consciousness

Abstract

Patients who survive brain injuries may develop Disorders of Consciousness (DOC) such as Coma, Vegetative State (VS) or Minimally Conscious State (MCS). Unfortunately, the rate of misdiagnosis between VS and MCS due to clinical judgment is high. Therefore, diagnostic decision support systems aiming to correct any differentiation between VS and MCS are essential for the characterization of an adequate treatment and an effective prognosis. In recent decades, there has been a growing interest in the new EEG computational techniques. We have reviewed how resting-state EEG is computationally analyzed to support differential diagnosis between VS and MCS in view of applicability of these methods in clinical practice. The studies available so far have used different techniques and analyses; it is therefore hard to draw general conclusions. Studies using a discriminant analysis with a combination of various factors and reporting a cut-off are among the most interesting ones for a future clinical application.

EEG Power spectra and subcortical pathology in chronic disorders of consciousness

“…Therefore, although we do find a consistent set of regions correlating with CRS-R communication subscale in the two studies, spanning left thalamus and putamen, the details of the associations between all subscales and subcortical brain pathology remain to be fully characterized. Third, consistent with prior work (Corchs et al, 2019; Estraneo et al, 2016; Lechinger et al, 2013), we find that EEG spectral features are relevant to diagnosing a patient's chronic state of consciousness (i.e. VS v. MCS), a decision known to be susceptible to a relatively high misdiagnosis rate (Monti et al, 2015; Monti & Owen, 2010; Schnakers et al, 2009; Schnakers, Giacino, Kalmar, Piret, & Lopez, 2006).…”

EEG functional connectivity is sensitive for nitrogen narcosis at 608 kPa

“…The output is an enormous 2-dimensional functional connectivity matrix between all channels, which is hard to interpret. However, connectivity results can be summarized with graph theory methodologies, to quantify the changes in the connectivity of a network 31 . It has been suggested that a neuronal network needs an optimal balance between integration and segregation to function fully and share information effectively between brain regions 30 .…”

Narrative Review: Quantitative EEG in Disorders of Consciousness

“…Hence, we provide an overview of the research over the last 20 years (2000-2020) of all the different parameters extracted from EEG recordings used for diagnosis and prognosis, and we summarize our findings in two tables at the end. Some existing reviews just focus on prognosis [24] or just present work conducted on resting-state EEG [25], [26]. Other reviews focus on BCI [27] or also EEG reactivity and transcranial magnetic stimulation (TMS)-EEG [28,29].…”